Abstract
It is well known that the photovoltaic (PV) device has a Maximum Power Point (MPP) that can ensure that maximum power is generated in a device. Since this MPP depends on solar radiation and the PV-panel temperature, it is never constant over time. A Maximum Power Point Tracker (MPPT) is widely used to ensure there is maximum power at all times. Almost all MPPT systems use a Perturbation and Observation (P&O) method because its simple procedure. If the solar radiation rapidly changes, however, the P&O efficiency degrades.
We propose a novel MPPT system to solve this problem that covers both the online-learning of the PV-properties and the feed-forward control of the DC-DC converter with a neural network. Both the simulation results and the actual device behaviors of our proposed MPPT method performed very efficiently even when the solar radiation rapidly changed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hiyama, T., Kitabayashi, K.: Neural network based estimation of maximum power generation from pv module using environmental information. IEEE Transactions on Energy Conversion 12(3), 241–247 (1997)
AbdulHadi, M., Al-Ibrahim, A.M., Virk, G.S.: Neuro-fuzzy-based solar cell model. IEEE Transactions on Energy Conversion 19(3), 619–624 (2004)
Akkaya, R., Kulaksiz, A.A., Aydogdu, O.: Dsp implementation of a pv system with ga-mlp-nn based mppt controller supplying bldc motor drive. Energy Conversion & Management 48, 210–218 (2007)
Specht, D.F.: A general regression neural network. IEEE Transactions on Neural Networks 2(6), 568–576 (1991)
Tomandl, D., Schober, A.: A modified general regression neural network (mgrnn) with a new efficient training algorithm as a robust “black-box”-tool for data analysis. Neural Networks 14, 1023–1034 (2001)
Su, M.-C., Lee, J., Hsieh, K.-L.: A new ARTMAP-based neural network for incremental learning. Neurocomputing 69, 2284–2300 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kohata, Y., Yamauchi, K., Kurihara, M. (2009). Quick Maximum Power Point Tracking of Photovoltaic Using Online Learning Neural Network. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_69
Download citation
DOI: https://doi.org/10.1007/978-3-642-10677-4_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
Online ISBN: 978-3-642-10677-4
eBook Packages: Computer ScienceComputer Science (R0)